FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method

TL;DR

FedMADE is a new federated learning approach that improves intrusion detection accuracy for minority attack classes in IoT networks by dynamically clustering devices and aggregating models, while maintaining robustness and low latency.

Contribution

Introduces FedMADE, a dynamic aggregation method for federated learning that addresses data heterogeneity and class imbalance in IoT intrusion detection.

Findings

Achieves up to 71.07% improvement in minority attack detection accuracy.

Demonstrates robustness against poisoning attacks.

Maintains low latency overhead of 4.7% compared to FedAvg.

Abstract

The rapid proliferation of Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. This has prompted ongoing research in Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for cyber-attack classification. Traditional ML models require data transmission from IoT devices to a centralized server for traffic analysis, raising severe privacy concerns. To address this issue, researchers have studied Federated Learning (FL)-based IDSs that train models across IoT devices while keeping their data localized. However, the heterogeneity of data, stemming from distinct vulnerabilities of devices and complexity of attack vectors, poses a significant challenge to the effectiveness of FL models. While current research focuses on adapting various ML models within the FL framework, they fail to effectively address the issue of attack class imbalance among devices, which significantly degrades the classification accuracy of minority attacks. To overcome this challenge, we introduce FedMADE, a novel dynamic aggregation method, which clusters devices by their traffic patterns and aggregates local models based on their contributions towards overall performance. We evaluate FedMADE against other FL algorithms designed for non-IID data and observe up to 71.07% improvement in minority attack classification accuracy. We further show that FedMADE is robust to poisoning attacks and incurs only a 4.7% (5.03 seconds) latency overhead in each communication round compared to FedAvg, without increasing the computational load of IoT devices.